The invention relates generally to imaging systems and more specifically to a system and method for segmentation lesions in medical images.
In many imaging systems such as computed tomography systems, for example, cross-sectional images or slices are made by an X-ray system, which are used for diagnosis. In positron emission tomography (PET) systems, for example, the patient is commonly injected with a biologically active radioactive tracer. The radioactive decay of the tracer emits a positron that annihilates with electrons in the body of the patient. This annihilation produces two high energy (about 511 KeV) photons propagating in nearly opposite directions (about 180 degrees apart) that are in coincidence. A detector and a computer system are used together for creating detailed images of a patient's organs and other body parts. The imaging capabilities are physically similar to those of X-ray systems, magnetic resonance imaging (MRI) systems, ultrasound systems, positron emission tomography (PET) systems, and other modalities similarly adapted to imaging certain tissues or anatomies.
The images generated by the imaging system are analyzed using imaging algorithms and pathologies of concern are highlighted which are then reviewed by radiologists for final diagnosis. As can be appreciated by those skilled in the art, certain subsequent imaging procedures may become feasible or may be recognized as desirable due to the improved management of data volume.
The images are typically analyzed for multiple features. For example, in oncology applications, the image is analyzed to locate and delineate lesions or tumors. Successful radiation therapy depends upon accurate delivery of a prescribed dose to the tumor while sparing as much as possible of the adjacent healthy tissue. The analysis of the images can be performed by various imaging techniques such as employing a segmentation scheme that quickly and accurately delineates tumor boundaries.
In most imaging systems employing segmentation schemes, experienced nuclear medicine physicians perform the tumor delineation manually. Such schemes are challenging for physicians due to small tumor sizes, blurred boundaries, inhomogeneous lesions and regions near to the lesion with similar image characteristics to the lesion. In addition, the method is time consuming and is a subjective process that is susceptible to fairly large inter and intra operator variations. Other segmentation schemes have been based primarily on determining a global threshold either manually or using data-driven classification. These methods, however, are not suitable to segment lesions that are located adjacent to other regions of high uptake and are sensitive to the dynamic range of the data.
Therefore, there is a need for developing a segmentation scheme that accurately identifies and delineates lesion boundaries in images in a robust and repeatable manner.